Stand-off Sensing Systems
Some sensor systems for detecting, localizing and tracking (DLT) targets, e.g., military vehicles such as tanks along a road, may utilize a handful of acoustic (or possibly others such as seismic) sensor arrays in a stand-off configuration from the targets of interest. Such systems, referred to as “stand-off” sensing systems, may sometimes utilize traditional beamforming techniques to ascertain lines of bearing to the targets, an association algorithm to associate lines of bearing and classification clues to localize the target(s), and a tracker to build and maintain target tracks.
Dense Distributed Sensing Systems
More recently, some sensor systems are being contemplated and developed that utilize a large number of inexpensive, densely spaced, sensors, which can blanket a large area where a target may be acquired by one or more of the sensors. These more recent systems may be referred to as “dense distributed” sensing systems. In one common implementation of a dense sensor system, one or more individual sensors (e.g. acoustic, seismic, IR motion, magnetic anomaly, bio-chemical, etc.) are co-located with and connected to support electronics (e.g., possibly a processor and communications hardware), and the combined sensor/electronics package is referred to as a sensor node. The sensor nodes communicate with each other through a communications network (e.g. radio or hardwired Ethernet). Alternately, the dense sensor system may have, instead of sensor nodes with a communications network, hardwired connections from each individual sensor directly to a processing module that may contain one processor, or several processors working in consort.
Current dense distributed sensing systems can have an algorithmic architecture that builds tracks for individual targets and may employ different sensor modalities for the tracking filter inputs. The algorithms may use simple measures such as time of Closest Point of Approach (CPA), Root-Mean-Square (RMS) intensity, etc., to locate and track one or more targets. In current instantiations, the algorithms typically use these measures to identify individual targets and build a track record for each individual target.
A common (but not exclusive) data processing chain for dense distributed sensing systems is that a sensor node processes data from its own sensors, and when it detects a target, the detection is compared to the list of known tracks and a decision is made as to whether the detection is associated with one of the known targets (i.e., an existing track), or is from a new target (i.e. a new track). In dense distributed sensing systems, the tracking may be done in the sensor node field; as a target travels past the sensor nodes, the communications network can distribute the latest track update in the general direction of travel, so that “downstream” sensor nodes have a track history to use for their data association and on which to build their track update.
Issues Associated with Large Target Counts and Increased Target Density
In both “stand-off” sensing systems and the current state of the art “dense distributed” sensing systems, the computation load can grow rapidly and track association can quickly become unmanageable as the target count increases and becomes denser. Tracking algorithms used in state of the art dense distributed sensing systems may be adequate for tracking an individual target or relatively small groups of targets. However, as the number of targets increases, the communications network traffic increases if the “track update” is transmitted to multiple sensor nodes ahead of the targets. In addition, the data association can become extremely complex and, in effect, intractable for very dense target fields, such as when convoys of targets may be moving in opposite directions along a road. Such “tracker overload” is an issue in stand-off sensing systems as well, since they also often build separate tracks for individual targets.